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A Survey on Generative Diffusion Model

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arxiv 2209.02646 v10 pith:BNH6BH2E submitted 2022-09-06 cs.AI

Hanqun Cao , Cheng Tan , Zhangyang Gao , Yilun Xu , Guangyong Chen , Pheng-Ann Heng , Stan Z. Li This is my paper · ORCID
classification cs.AI
keywords diffusiongenerativemodelscreativityhumanprofoundsurveya-survey-on-generative-diffusion-model
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Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented in https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Pre-training an EfficientNet classifier on GAN-generated balanced hand images, then fine-tuning on real data, raises accuracy on the imbalanced RWTH handshape benchmark from 80.6% to 85.3%.

  2. Cloud Diffusion Part 1: Theory and Motivation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Replacing white noise with scale-invariant noise tuned to an image set's power-law statistics could make diffusion models faster, sharper, and more controllable, this theory paper argues.

  3. Image Watermarking of Generative Diffusion Models

    eess.IV 2025-02 reject novelty 4.0 of 10

    A new watermarking scheme for diffusion models trains an autoencoder to embed and recover image watermarks through the generation process, but the reported robustness is undermined by flawed evaluation and an unjustif...

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